Moore’s law essentially refers to the exponential growth of electronic industry and surprisingly, the present scenario has contradicted the law’s nature by slowing down the process of expansion. Many in the tech industry have started believing that the law holds no truth in it whatsoever. However, this was the case only until Artificial Intelligence had […]

Moore’s law essentially refers to the exponential growth of electronic industry and surprisingly, the present scenario has contradicted the law’s nature by slowing down the process of expansion.

Many in the tech industry have started believing that the law holds no truth in it whatsoever. However, this was the case only until Artificial Intelligence had transpired into the scene. Now, AI is steadily revamping the status of Moore’s law and its significance.

Back in 1965, Gordon Moore, one of the co-founders of Intel noted that the costs of integrated circuits had reduced to half of the original value whilst the number of transistors doubled in amount on a yearly basis. This inverse variation had no influence on speed, however. Gordon would then go about and confirm that this trend was bound to persist in the near future as well.

Although Moore’s law might seemingly portray its weakness in clarity quite explicitly, the technology-scaling rule called Dennard Scaling provided necessary support in this regard. The technology-scaling rule revolves around the fact that when number of transistors is increased per unit area, power requirement per unit area will remain constant and the current will scale downward with length.

The size of the transistors has been reduced but the energy efficiency and speed have increased manifold. All these advantages have not influenced the budget of these devices whatsoever.

Chien-Ping Lu of NovuMind Inc points out that, “Moore’s law is dying gradually” in his paper where he also elaborates the reason as in how transistors continued to double in overall amount after 2005 whilst the energy efficiency dwindled.

The tech industry flourished in 21st century with increase in the usage of smart devices and other equipment that made life better and easy. Qualcomm and Samsung are such splendid cases of technological boom in the industry of processors and smart phones.

The boost in technology relatively resulted in the surge of competing firms. The demand and challenges increased as every competitive firm in the market had to launch better performing hardware to meet the specifications that are resolute in yielding enhanced performance.

Therefore, user experience was placed as the foremost priority. Nevertheless, new versions of these devices lagged in terms of saving energy.

The fluctuation from Moore’s law’s failure has slowly become viable after the break down of the Dennard scaling aspect. Artificial Intelligence plays the crucial role here in revamping Moore’s law.

The growth of Artificial Intelligence reminds us of the design of over-hyped smart robots that could potentially be machines with thinking capacities, but is the exaggerated portrayal by media.

Alan Turing, a pioneer in this aspect believed that the process of creating machines with thinking capacity was very difficult and hence he put forth the theory that programming machines with the thinking capacity of a child was a gateway to reach the design of machine which has the thinking capacity of an adult.

Currently in the era of AI, we are gathering huge amounts of data and we are developing complicated algorithms. Although, we have the required methodologies to move in a specific direction we lack in the aspect of hardware. Moreover, the possibility of achieving this feat could result in causing another Global warming with the usage of GPUs. It’s a rotational cycle of wanting more processing power leading to the design of advanced algorithms and hence requiring large amounts of data.

Artificial Intelligence is helping to enhance performance while maintaining the power consumption and price at a very minimal level. This corresponds to the Dennard scaling again and thereby bringing Moore’s law back to life.

Nvidia added nine new GPU- charged supercomputers container to there cloud services. Nvidia has expanded its Nvidia GPU Cloud to 35 containers and they have also made there about triple than previous year launch. Nvidia is aiming to help most dedicated engineers who are running heavy workloads which use machine learning crush there mathematical equations […]

Nvidia added nine new GPU- charged supercomputers container to there cloud services. Nvidia has expanded its Nvidia GPU Cloud to 35 containers and they have also made there about triple than previous year launch.

Nvidia is aiming to help most dedicated engineers who are running heavy workloads which use machine learning crush there mathematical equations for training or running models. Developers are free to operate their programmes with their choice of framework after that they can apply on the model of the shared clusters so that those models can runn efficiently.

NCG have different kinds of packages according to the compatibility of the application. “PIG compilers are available for containers on NGC for helping developers to make HPC applications for targeting multicore CPU’s and Nvidia Tesla GPU. Different tools and PIG compilers for using portable HPC applications to develop performance, through usable OpenACC, OpenMP, with CUDA Fortran straight programming” said Nvidia in a article.

AMBER is molecular simulation and even CANDLE for doing cancer research for like CHROMA optimizing maths and physics model. NGC’s container now have more than 27,000 registered users.

After November’s Supercomputing conference, they added nine newly developed HPC and Visualization container which includes CANDLE, VMD, CHROMA and PGI was added in NGC. This one is going to be additional to eight containers including NAMD, ParaView and GROMACS was the part of pervious year’s supercomputing conference. This is like the benchmark for the developers but again getting the top position this conference was successful.

These containers are making more easier for developers because they don’t require to install all the imperative different libraries and back-up programs to deploy container models. These are nice and useful for the purpose of testing models so that researchers can do there experiments with various different systems and to identify the result from the experiment.

Nvidia is also testing different GPU connected power workspace like Nvidia’s DGX’s with many other cloud platforms like Amazon Web Services, Oracle, Google Cloud and Cloud Infrastructure. They are doing best to provide best service for all the users so that they can perform in better way. They are planning to give much better containers next year in the supercomputing conference. “We are doing for the benefit of the elite companies and society so that they can apply to type of database intelligence. Artificial intelligence is creating more excellence for the users.” said Nvidia

With the rise of bitcoin mining, the current time is considered unsuitable for building games for personal computers. The major components for building PC gaming are being rendered unavailable by the bitcoin miners. GPUs for PC Game-building process has become quite easy, and playing games on computers has also become quite a delightful experience for […]

With the rise of bitcoin mining, the current time is considered unsuitable for building games for personal computers. The major components for building PC gaming are being rendered unavailable by the bitcoin miners.

GPUs for PC

Game-building process has become quite easy, and playing games on computers has also become quite a delightful experience for gamers. However, the graphics cards, or GPUs, which are crucial for powering the visuals of computers, are in in high demand, and they are not easy to find any more. Their price has risen considerably from the intended mark. A large number of resellers are hiking the price, going as much as double or even more.

Reason behind the demand

The demand for the GOUs is not because of the huge influx in the number of gamers. And bitcoin mining largely drive this demand for PC gaming among enthusiasts. While brands like AMD or Nvidia sell graphics cards whch have the ability to power high tech gaming visuals, these cards are also able to mine bitcoins. In fact, they are among the major tools that the miners use.

Results on the price

The graphics cards are on sale almost everywhere, and this is resulting in the same outcome at every retails site, from Amazon to Best Buy: that they are sold out, that the resellers are stuck with big markups, or options of back-order. GTX 1080Ti, the flagship card by Nvdia, is now available at a retail cost of $700. Amazon’s lowest price is $1,100. It is now hard to find them at a decent price, so much so that the subject has become a joke.

Solution of this problem

Bitcoin Minings has become extremely popular now, and this is obviously turning out to be a problem. However, as more people are becoming aware of the concept, and they need to be in acquisition of proper ardware that can power this activity, the problem is likely to be solved soon by the GPU manufacturers. As the manufacturers of graphics cards are getting ready to supply the cards swiftly, it is likely to get solved soon. Asus, AMD, Nvidia, etc. are doing the same. There’s an identifiable movement by the brands in works getting done to bring the right solution in this case.

There is obviously a lot of confusion in this area, and the gaming brands are also facing some problems. But the overall production of graphics cards will not only help the gaming industry soon, but the bitcoin miners as well, which will, in turn, enhance the experience of the gamers.